Images and Displays. Lecture Steve Marschner 1
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1 Images and Displays Lecture Steve Marschner 1
2 Introduction Computer graphics: The study of creating, manipulating, and using visual images in the computer.
3 What is an image? A photographic print? Pixels? Some numbers in RAM? 2008 Steve Marschner 3
4 An image is: A 2D distribution of intensity or color A function defined on a two-dimensional plane Note: no mention of pixels yet To do graphics, must: represent images encode them numerically Image files such as jpg, png, svg files display images realize them as actual intensity distributions 2008 Steve Marschner 4
5 Representative display technologies Computer displays Raster CRT display LCD display OLED display, DLP projector, Printers Laser printer Inkjet printer 2008 Steve Marschner 5
6 Cathode ray tube First widely used electronic display [H&B fig. 2-2] developed for TV in the 1920s 1930s 2008 Steve Marschner 6
7 LCD flat panel or projection display block or transmit light by twisting its polarization [H&B fig. 2-16] Intermediate intensity levels possible by partial twist 2008 Steve Marschner 7
8 OLED display An OLED is a light-emitting diode In low ambient light conditions such as a dark room, an OLED can achieve a much higher contrast ratio than an LCD A demonstration of a flexible OLED device (from Wikipedia)
9 DLP projector DMD Digital light processing The image is created by microscopically small mirrors laid out in a matrix on a semiconductor chip, known as a Digital Micromirror Device (DMD)
10 Color displays (light emiting devices) Operating principle: humans are trichromatic match any color with blend of three therefore, problem reduces to producing 3 images and blending green Additive color (as opposed to subtractive color) yellow cyan red white blue magenta [source unknown] blend images by sum e.g. overlapping projection R, G, B make good primaries R=(1,0,0) G=(0,1,0) B=(0,0,1) Cyan=G+B=(0,1,1) 2008 Steve Marschner 10
11 Color displays [H&B fig. 2- interleaved R,G,B pixels (except DLPs) 2008 Steve Marschner
12 Electronic paper (E Ink) In an extremely bright condition, E-ink outperforms all light-emitting displays An e-paper display visible in the sunlight
13 Laser printer Key characteristics image is binary resolution is high very small, isolated dots are not possible QuickTime and a TIFF (Uncompressed) decompressor are needed to see this picture. [howstuffworks.com Like a photocopier but with laser-scanned raster as source image 2008 Steve Marschner 13
14 Inkjet printer Liquid ink sprayed in small drops Head with many jets scans across paper Key characteristics: [source unknown] image is binary (drop or no drop; no partial drops) isolated dots are reproduced well 2008 Steve Marschner 14
15 Digital camera A raster input device [dpreview.com] [CS 417 Spring 2002] Image sensor contains 2D array of photosensors 2008 Steve Marschner 15
16 Digital camera [Foveon] Color typically captured using color mosaic 2008 Steve Marschner 16
17 Raster image representation (Pixels as opposed to vectors) All these devices suggest 2D arrays of numbers (pixels) Big advantage: represent arbitrary images [Philip Greenspun] approximate arbitrary functions with increasing resolution works because memory is cheap (brute force approach!) 2008 Steve Marschner 17
18 Datatypes for raster images Bitmaps: boolean per pixel (1 bpp): black and white Grayscale: integer per pixel: shades of gray; e.g. black-and-white print precision: usually byte (8 bpp); sometimes 10, 12, or 16 bpp E-ink: 4bpp (16 grey levels) Color: 3 integers per pixel: full range of displayable color precision: usually byte[3] (24 bpp) sometimes 16 (5+6+5) or 30 or 36 or 48 bpp indexed color (palletized color : usually 8bpp): a fading idea. GIF format 2008 Steve Marschner 18
19 Datatypes for raster images Floating point: more precision provides high dynamic range (HDR) 2008 Steve Marschner 19
20 HDR - 72bpp image 24bpp image Higher bits Lower bits
21 HDR images are good for post-processing (exposure compensation) - badly taken photos can be recovered
22 image: Paul Debevec exposure: -8 stops 2008 Steve Marschner
23 image: Paul Debevec exposure: +0 stops 2008 Steve Marschner 23
24 image: Paul Debevec exposure: +6 stops 2008 Steve Marschner 24
25 Datatypes for raster images Floating point: more precision provides high dynamic range (HDR) represent real scenes independent of display becoming the standard intermediate format in graphics processors 2008 Steve Marschner 25
26 Datatypes for raster images For color or grayscale, sometimes add alpha channel without and with alpha [Adobe Photoshop sample] describes transparency of images more on this in a few lectures 2008 Steve Marschner 26
27 Storage requirements for images 1024x1024 image (1 megapixel) bitmap: 128KB grayscale 8bpp: 1MB grayscale 16bpp: 2MB color 24bpp: 3MB floating-point HDR color: 12MB 2008 Steve Marschner 27
28 Image processing Color to gray Down pixel precision (16bpp 4bpp) Brute-force Dithering Gamma correction
29 COLOR Converting pixel formats Color to gray could take one channel (blue, say) leads to odd choices of gray value BLUE ONLY combination of channels is better but different colors contribute differently to lightness which is lighter, full blue or full green? good choice: gray = 0.2 R G B more on this in color, later on Same pixel values. GRAY Same luminance? 2008 Steve Marschner 29
30 Converting pixel precision 8 bpp (256 grays) [photo: Philip Greenspun] Up is easy; down loses information be careful 2008 Steve Marschner 30
31 Converting pixel precision 8 7bpp (256 bpp 5grays) bpp (128 (32 grays) grays) [photo: Philip Greenspun] Up is easy; down loses information be careful 2008 Steve Marschner 31
32 Converting pixel precision 8 7bpp (256 bpp (128 5grays) bpp 4grays) bpp(32 (16grays) grays) [photo: Philip Greenspun] Up is easy; down loses information be careful 2008 Steve Marschner 32
33 Converting pixel precision 8 bpp (256 bpp ( bpp (64 grays) 5grays) bpp (32 grays) 4grays) bpp (16 grays) 3 bpp (8 grays) [photo: Philip Greenspun] Up is easy; down loses information be careful 2008 Steve Marschner 33
34 Converting pixel precision 8 bpp (256 bpp ( bpp (64 grays) 5grays) bpp (32 grays) 4grays) bpp (16 grays) 3 2bpp (8 grays) bpp (4 grays) [photo: Philip Greenspun] Up is easy; down loses information be careful 2008 Steve Marschner 34
35 Converting pixel precision Up is easy; down loses information be careful 8 bpp (256 bpp ( bpp (64 grays) 5grays) bpp (32 grays) 4grays) bpp (16 grays) 3 2bpp (8 bpp (4 1 bpp grays) (2grays) grays) [photo: Philip Greenspun] Lacks gradation 2008 Steve Marschner 35
36 Dithering When decreasing bpp, we quantize I =0 if I <0.5 I =1 if I >= 0.5 Instead, be inconsistent dither turn on some pixels but not others in gray regions a way of trading spatial for tonal resolution Laser printed image WITHOUT dithering [Philip Greenspun] Make choices consistently: banding artifacts 2008 Steve Marschner 36
37 Dithering methods Ordered dither produces larger dots laser printer takes advantage of devices that can reproduce isolated dots inkjet, screen [Philip Greenspun] Diffusion dither 2008 Steve Marschner 37
38 Ordered Dither example [photo: Philip Greenspun] Produces regular grid of compact dots 2008 Steve Marschner 38
39 Diffusion dither [photo: Philip Greenspun] Produces scattered dots with the right local density 2008 Steve Marschner 39
40 Pixel Values vs Light Intensity What do the numbers in images (pixel values) mean? they determine how bright that pixel is bigger numbers are (usually) brighter Is a pixel with value 200 twice as bright as another pixel with value 100?
41 What this projector does n = 64 n = 128 n = 192 I = 0.25 I = 0.5 I = Steve Marschner 41
42 What this projector does n = 64 n = 128 n = 192 I = 0.25 I = 0.5 I = Steve Marschner 42
43 What this projector does n = 64 n = 128 n = 192 I = 0.25 I = 0.5 I = Steve Marschner 43
44 What this projector does Something like this if the blue boxed region look brighter than the red box (n=128:) Why? Boxed region: I=0.5 (Imax+Imin) I(128)<0.5 (Imax+Imin) Light intensity Imax+Imin n=128 =255 Pixel values 2008 Steve Marschner 44
45 What do the numbers in images (pixel values) mean? they determine how bright that pixel is bigger numbers are (usually) brighter Transfer function: function that maps input pixel value to luminance of displayed image What determines this function? physical constraints of device or medium desired visual characteristics 2008 Steve Marschner 45
46 Constraints on transfer function (for light-emitting displays) Maximum displayable intensity, Imax how much power can be channeled into a pixel? LCD: backlight intensity, transmission efficiency (<10%) projector: lamp power, efficiency of imager and optics Minimum displayable intensity, Imin light emitted by the display in its off state e.g. stray electron flux in CRT, polarizer quality in LCD 2008 Steve Marschner 46
47 Viewing flare, k: light reflected by the display very important factor determining image contrast in practice 5% of Imax is typical in a normal office environment [srgb spec] much effort to make very black CRT and LCD screens all-black decor in movie theaters 2008 Steve Marschner 47
48 Dynamic range Dynamic range Rd = Imax / Imin, or (Imax + k) / (Imin + k) determines the degree of image contrast that can be achieved a major factor in image quality Ballpark values Desktop display in typical conditions: 20:1 Photographic print: 30:1 Desktop display in good conditions: 100:1 Desktop display in a dark room: 1000:1 ~ inf:1 Photographic transparency (directly viewed): 1000:1 High dynamic range display in good conditions: 10,000:1 (very bright white) 2008 Steve Marschner 48
49 Transfer function shape Desirable property: the change from one pixel value to the next highest pixel value should not produce a visible contrast [Philip Greenspun] otherwise smooth areas of images will show visible bands What contrasts are visible? rule of thumb: under good conditions we can notice a 2% change in intensity therefore we generally need smaller quantization steps in the darker tones than in the lighter tones most efficient quantization is logarithmic an image with severe banding 2008 Steve Marschner 49
50 How many levels are needed? Ideal case : exponential quantization Depends on dynamic range 2% steps are most efficient: log 1.02 is about 1/120, so 120 steps are needed for 10 times intensity 240 steps are needed for 100:1 dynamic range Moral: 8 bits is just barely enough for low-end applications even if we are careful about quantization 2008 Steve Marschner 50
51 Intensity quantization in practice Option 1: linear quantization pro: simple, convenient, amenable to arithmetic con: requires more steps (wastes memory) need 12 bits for any useful purpose; more than 16 for HDR Option 2: Ideal exponential quantization Too expensive I ( n)=(1.02)n I min Option 3: power-law quantization pro: fairly simple, approximates ideal exponential quantization con: need to linearize before doing pixel arithmetic con: need to agree on exponent (standard gamma: 2.2) 8 bits are OK for many applications 2008 Steve Marschner 51
52 Option3 : Gamma quantization ~ ~ Close enough to ideal perceptually uniform exponential 2008 Steve Marschner 52
53 Gamma correction Sometimes (often, in graphics) we have computed intensities a that we want to display linearly In the case of an ideal monitor with zero black level, (where N = 2n 1 in n bits). Solving for n: This is the gamma correction recipe that has to be applied when computed values are converted to 8 bits for output failing to do this (implicitly assuming gamma = 1) results in dark, oversaturated images 2008 Steve Marschner 53
54 [Philip Greenspun] Gamma correction corrected for γ lower than display OK corrected for γ higher than display 2008 Steve Marschner 54
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